Stochastic learning paths in a knowledge structure
Journal of Mathematical Psychology
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Knowledge Spaces
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Graph-based hierarchical conceptual clustering
The Journal of Machine Learning Research
Digital game-based learning (DGBL) model and development methodology for teaching history
WSEAS Transactions on Computers
Ontology versus semantic networks for medical knowledge representation
ICCOMP'08 Proceedings of the 12th WSEAS international conference on Computers
Realization of E-University for distance learning
WSEAS Transactions on Computers
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Item hierarchy and concept tree provide references for cognition diagnosis and remedial instruction. Therefore, integration of data analysis on item hierarchy and concept tree should be important. The purpose of this study is to provide an integrated methodology of item hierarchy and concept tree analysis. Besides, fuzzy clustering is adopted to classify sample so that homogeneity appear in the same cluster and adaptive instruction will be more feasible. Polytomous item relational structure (PIRS) is the foundation of item hierarchy analysis. Interpretive structural modeling (ISM) combined with calculation of ordering coefficient is to construct concept tree. Source data sets of PIRS and ISM are based on response data matrix and item-attribute matrix respectively. In this study, the empirical test data is the statistics assessment of university students. The results show that the integration of PIRS and ISM based on fuzzy clustering are useful for cognition diagnosis and adaptive instruction. Finally, further suggestions and recommendations based on findings are discussed.